Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
What an ML-ful World! MLKit for Android dev.
Search
Britt Barak
October 12, 2018
Programming
0
130
What an ML-ful World! MLKit for Android dev.
Britt Barak
October 12, 2018
Tweet
Share
More Decks by Britt Barak
See All by Britt Barak
[Vonage] Introducing Conversations
brittbarak
1
130
Kids, Play Nice! Kotlin-Java Interop In Mind
brittbarak
2
450
Sharing is Caring- Getting Started with Kotlin Multiplatform
brittbarak
2
2.1k
Between JOMO and FOMO: You are reshaping communication.
brittbarak
2
1.2k
Build Apps For The Ones You Love
brittbarak
1
120
Make your app dance with MotionLayout
brittbarak
8
1.4k
Who's afraid of ML? V2 : First steps with MlKit
brittbarak
1
460
Oh, the places you'll go! Cracking Navigation on Android
brittbarak
0
480
The organic evolution - how and why we created peer mentorship program
brittbarak
0
54
Other Decks in Programming
See All in Programming
Ruby Parser progress report 2025
yui_knk
1
430
AWS発のAIエディタKiroを使ってみた
iriikeita
1
180
私の後悔をAWS DMSで解決した話
hiramax
4
210
Putting The Genie in the Bottle - A Crash Course on running LLMs on Android
iurysza
0
140
JSONataを使ってみよう Step Functionsが楽しくなる実践テクニック #devio2025
dafujii
1
520
@Environment(\.keyPath)那么好我不允许你们不知道! / atEnvironment keyPath is so good and you should know it!
lovee
0
110
Deep Dive into Kotlin Flow
jmatsu
1
310
ソフトウェアテスト徹底指南書の紹介
goyoki
1
150
今だからこそ入門する Server-Sent Events (SSE)
nearme_tech
PRO
0
110
ファインディ株式会社におけるMCP活用とサービス開発
starfish719
0
290
はじめてのMaterial3 Expressive
ym223
2
250
testingを眺める
matumoto
1
140
Featured
See All Featured
Why Our Code Smells
bkeepers
PRO
339
57k
Making the Leap to Tech Lead
cromwellryan
135
9.5k
Building an army of robots
kneath
306
46k
[Rails World 2023 - Day 1 Closing Keynote] - The Magic of Rails
eileencodes
36
2.5k
The Cult of Friendly URLs
andyhume
79
6.6k
Speed Design
sergeychernyshev
32
1.1k
Side Projects
sachag
455
43k
Become a Pro
speakerdeck
PRO
29
5.5k
Dealing with People You Can't Stand - Big Design 2015
cassininazir
367
27k
Large-scale JavaScript Application Architecture
addyosmani
512
110k
Connecting the Dots Between Site Speed, User Experience & Your Business [WebExpo 2025]
tammyeverts
8
520
Done Done
chrislema
185
16k
Transcript
What an ML-ful world Britt Barak
Once upon a time @BrittBarak
beta @BrittBarak
ML Capability ?! @BrittBarak
Who is afraid of Machine Learning? & First Steps With
ML-Kit @BrittBarak
Britt Barak Developer Experience, Nexmo Google Developer Expert Britt Barak
@brittBarak
None
@BrittBarak
= @BrittBarak
§ What’s the difference? @BrittBarak
…and classify? @BrittBarak
@BrittBarak
This is a strawberry @BrittBarak
This is a strawberry Red Seeds pattern Narrow top leaves
@BrittBarak Pointy at the bottom Round at the top
Strawberry Not Not Not Strawberry Strawberry Not Not Not @BrittBarak
~*~ images ~*~ @BrittBarak
@BrittBarak Vision library
Text Recognition @BrittBarak
Face Detection @BrittBarak
Barcode Scanning @BrittBarak
Image Labelling @BrittBarak
Landmark Recognition @BrittBarak
Custom Models @BrittBarak
Example @BrittBarak
@BrittBarak
@BrittBarak
Detector detector .execute(image) Result: @BrittBarak “Ben & Jerry’s pistachio ice
cream”
1. Setup Detector @BrittBarak
Local or cloud? @BrittBarak
@BrittBarak
Local •Realtime •Offline support •Security / Privacy •Bandwith •… @BrittBarak
Cloud •More accuracy & features •But more latency •Pricing @BrittBarak
1. Setup Detector @BrittBarak
Text Detector textDetector = FirebaseVision.getInstance() @BrittBarak
Text Detector textDetector = FirebaseVision.getInstance() .onDeviceTextRecognizer @BrittBarak
Text Detector textDetector = FirebaseVision.getInstance() .cloudTextRecognizer @BrittBarak
2. Process input @BrittBarak
FirebaseVisionImage •Bitmap •image Uri •Media Image •byteArray •byteBuffer @BrittBarak
image = FirebaseVisionImage.fromBitmap(bitmap) @BrittBarak Text Detector
3. Execute the model @BrittBarak
Text Detector textDetector.processImage(image) @BrittBarak
Text Detector textDetector.processImage(image) .addOnSuccessListener { } @BrittBarak
Text Detector textDetector.processImage(image) .addOnSuccessListener { firebaseVisionTexts -> processOutput(fbVisionTexts) } @BrittBarak
4. Process output @BrittBarak
firebaseVisionTexts.text @BrittBarak
someTextView.text = firebaseVisionTexts.text @BrittBarak UI
Result @BrittBarak
Result @BrittBarak
(another) Example : Labelling @BrittBarak
Detector detector .execute(image) Result: @BrittBarak ice cream pint
Vegetables Deserts Vegetables
1. Setup Detector @BrittBarak
Image Classifier imageDetector = FirebaseVision.getInstance() @BrittBarak
Image Classifier imageDetector = FirebaseVision.getInstance() .visionLabelDetector @BrittBarak
Image Classifier imageDetector = FirebaseVision.getInstance .visionCloudLabelDetector @BrittBarak
2. Process input @BrittBarak
image = FirebaseVisionImage.fromBitmap(bitmap) @BrittBarak Image Classifier
3. Execute the model @BrittBarak
Image Classifier imageDetector.detectInImage(image) @BrittBarak
Image Classifier imageDetector.detectInImage(image) .addOnSuccessListener{ } @BrittBarak
Image Classifier imageDetector.detectInImage(image) .addOnSuccessListener{ fBLabels -> processOutput(fBLabels) } @BrittBarak
4. Process output @BrittBarak
fbLabel.label fbLabel.confidence fbLabel.entityId @BrittBarak
UI for (fbLabel in labels) { s = "${fbLabel.label} :
${fbLabel.confidence}" } @BrittBarak
Result
Result
It is an ML-ful world Enjoy!
Thank you! Keep in touch!